Overview

Dataset statistics

Number of variables15
Number of observations5005
Missing cells0
Missing cells (%)0.0%
Duplicate rows198
Duplicate rows (%)4.0%
Total size in memory586.6 KiB
Average record size in memory120.0 B

Variable types

Categorical1
Numeric14

Alerts

Dataset has 198 (4.0%) duplicate rowsDuplicates
Max Speed(m/s) is highly correlated with Transportation Mode and 9 other fieldsHigh correlation
95% Speed(m/s) is highly correlated with Transportation Mode and 9 other fieldsHigh correlation
75% Speed(m/s) is highly correlated with Transportation Mode and 7 other fieldsHigh correlation
Mean Speed(m/s) is highly correlated with Transportation Mode and 6 other fieldsHigh correlation
Speed Std is highly correlated with Transportation Mode and 8 other fieldsHigh correlation
Max Acceleration(m/s^2) is highly correlated with Max Speed(m/s) and 8 other fieldsHigh correlation
95% Acceleration(m/s^2) is highly correlated with Transportation Mode and 5 other fieldsHigh correlation
75% Acceleration(m/s^2) is highly correlated with Transportation Mode and 6 other fieldsHigh correlation
Mean Acceleration(m/s^2) is highly correlated with Transportation Mode and 7 other fieldsHigh correlation
Acceleration Std is highly correlated with Max Acceleration(m/s^2) and 4 other fieldsHigh correlation
Non 0 Mean Speed(m/s) is highly correlated with Transportation Mode and 6 other fieldsHigh correlation
Non 0 Mean Acceleration(m/s^2) is highly correlated with Transportation Mode and 7 other fieldsHigh correlation
Total Time(s) is highly correlated with Total Distance(m)High correlation
Total Distance(m) is highly correlated with Max Speed(m/s) and 6 other fieldsHigh correlation
Transportation Mode is highly correlated with Max Speed(m/s) and 9 other fieldsHigh correlation
Total Distance(m) is highly skewed (γ1 = 37.15879244) Skewed
Max Acceleration(m/s^2) has 80 (1.6%) zeros Zeros
95% Acceleration(m/s^2) has 86 (1.7%) zeros Zeros
75% Acceleration(m/s^2) has 325 (6.5%) zeros Zeros
Mean Acceleration(m/s^2) has 543 (10.8%) zeros Zeros
Acceleration Std has 584 (11.7%) zeros Zeros
Non 0 Mean Acceleration(m/s^2) has 546 (10.9%) zeros Zeros

Reproduction

Analysis started2022-10-24 04:12:01.896089
Analysis finished2022-10-24 04:12:17.462615
Duration15.57 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Transportation Mode
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
walk
1988 
bus
1130 
bike
667 
car
585 
subway
381 
Other values (6)
254 

Length

Max length10
Median length4
Mean length3.832567433
Min length3

Characters and Unicode

Total characters19182
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbus
2nd rowbus
3rd rowwalk
4th rowbus
5th rowwalk

Common Values

ValueCountFrequency (%)
walk1988
39.7%
bus1130
22.6%
bike667
 
13.3%
car585
 
11.7%
subway381
 
7.6%
taxi177
 
3.5%
train52
 
1.0%
airplane14
 
0.3%
run5
 
0.1%
boat4
 
0.1%

Length

2022-10-23T21:12:17.508095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
walk1988
39.7%
bus1130
22.6%
bike667
 
13.3%
car585
 
11.7%
subway381
 
7.6%
taxi177
 
3.5%
train52
 
1.0%
airplane14
 
0.3%
run5
 
0.1%
boat4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a3215
16.8%
k2655
13.8%
w2369
12.4%
b2182
11.4%
l2004
10.4%
u1516
7.9%
s1511
7.9%
i910
 
4.7%
e683
 
3.6%
r658
 
3.4%
Other values (8)1479
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19182
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3215
16.8%
k2655
13.8%
w2369
12.4%
b2182
11.4%
l2004
10.4%
u1516
7.9%
s1511
7.9%
i910
 
4.7%
e683
 
3.6%
r658
 
3.4%
Other values (8)1479
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin19182
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3215
16.8%
k2655
13.8%
w2369
12.4%
b2182
11.4%
l2004
10.4%
u1516
7.9%
s1511
7.9%
i910
 
4.7%
e683
 
3.6%
r658
 
3.4%
Other values (8)1479
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII19182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a3215
16.8%
k2655
13.8%
w2369
12.4%
b2182
11.4%
l2004
10.4%
u1516
7.9%
s1511
7.9%
i910
 
4.7%
e683
 
3.6%
r658
 
3.4%
Other values (8)1479
7.7%

Max Speed(m/s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1965
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.52017982
Minimum0.02
Maximum291.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:17.571970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile1.2
Q11.95
median4.99
Q313.97
95-th percentile27.372
Maximum291.36
Range291.34
Interquartile range (IQR)12.02

Descriptive statistics

Standard deviation12.87895089
Coefficient of variation (CV)1.352805423
Kurtosis183.690312
Mean9.52017982
Median Absolute Deviation (MAD)3.61
Skewness10.10658402
Sum47648.5
Variance165.867376
MonotonicityNot monotonic
2022-10-23T21:12:17.639865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3825
 
0.5%
1.6122
 
0.4%
1.5922
 
0.4%
1.4821
 
0.4%
1.4121
 
0.4%
1.5121
 
0.4%
1.521
 
0.4%
1.6820
 
0.4%
1.7520
 
0.4%
1.4720
 
0.4%
Other values (1955)4792
95.7%
ValueCountFrequency (%)
0.021
< 0.1%
0.092
< 0.1%
0.131
< 0.1%
0.192
< 0.1%
0.241
< 0.1%
0.272
< 0.1%
0.31
< 0.1%
0.311
< 0.1%
0.321
< 0.1%
0.332
< 0.1%
ValueCountFrequency (%)
291.361
< 0.1%
259.451
< 0.1%
255.431
< 0.1%
246.721
< 0.1%
243.261
< 0.1%
241.251
< 0.1%
234.791
< 0.1%
81.611
< 0.1%
74.011
< 0.1%
65.211
< 0.1%

95% Speed(m/s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1825
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.318693307
Minimum0.02
Maximum270.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:17.710541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile1.11
Q11.71
median4.66
Q311.69
95-th percentile23.598
Maximum270.7
Range270.68
Interquartile range (IQR)9.98

Descriptive statistics

Standard deviation11.88726517
Coefficient of variation (CV)1.428982261
Kurtosis226.4463687
Mean8.318693307
Median Absolute Deviation (MAD)3.31
Skewness11.68426638
Sum41635.06
Variance141.3070732
MonotonicityNot monotonic
2022-10-23T21:12:17.781700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3430
 
0.6%
1.5629
 
0.6%
1.4927
 
0.5%
1.625
 
0.5%
1.4425
 
0.5%
1.4625
 
0.5%
1.4225
 
0.5%
1.5524
 
0.5%
1.3223
 
0.5%
1.523
 
0.5%
Other values (1815)4749
94.9%
ValueCountFrequency (%)
0.021
 
< 0.1%
0.092
< 0.1%
0.131
 
< 0.1%
0.161
 
< 0.1%
0.172
< 0.1%
0.231
 
< 0.1%
0.262
< 0.1%
0.291
 
< 0.1%
0.33
0.1%
0.311
 
< 0.1%
ValueCountFrequency (%)
270.71
< 0.1%
250.641
< 0.1%
249.631
< 0.1%
243.241
< 0.1%
242.491
< 0.1%
236.221
< 0.1%
231.391
< 0.1%
59.271
< 0.1%
57.631
< 0.1%
57.461
< 0.1%

75% Speed(m/s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1531
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.124823177
Minimum0
Maximum257.21
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:17.850915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.792
Q11.35
median3.6
Q37.72
95-th percentile18.23
Maximum257.21
Range257.21
Interquartile range (IQR)6.37

Descriptive statistics

Standard deviation10.62300612
Coefficient of variation (CV)1.734418418
Kurtosis324.9011419
Mean6.124823177
Median Absolute Deviation (MAD)2.4
Skewness15.10713944
Sum30654.74
Variance112.8482591
MonotonicityNot monotonic
2022-10-23T21:12:17.922450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3846
 
0.9%
1.2742
 
0.8%
1.439
 
0.8%
1.535
 
0.7%
1.4433
 
0.7%
1.3132
 
0.6%
1.0632
 
0.6%
1.4632
 
0.6%
1.4232
 
0.6%
1.3231
 
0.6%
Other values (1521)4651
92.9%
ValueCountFrequency (%)
01
 
< 0.1%
0.011
 
< 0.1%
0.042
 
< 0.1%
0.071
 
< 0.1%
0.081
 
< 0.1%
0.12
 
< 0.1%
0.111
 
< 0.1%
0.125
0.1%
0.141
 
< 0.1%
0.151
 
< 0.1%
ValueCountFrequency (%)
257.211
< 0.1%
245.041
< 0.1%
242.441
< 0.1%
241.541
< 0.1%
234.091
< 0.1%
231.221
< 0.1%
221.231
< 0.1%
45.332
< 0.1%
42.871
< 0.1%
40.351
< 0.1%

Mean Speed(m/s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1240
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.412161838
Minimum0.01
Maximum240.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:17.991960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.58
Q11.1
median2.78
Q35.56
95-th percentile12.598
Maximum240.8
Range240.79
Interquartile range (IQR)4.46

Descriptive statistics

Standard deviation8.728402687
Coefficient of variation (CV)1.978259866
Kurtosis442.5095749
Mean4.412161838
Median Absolute Deviation (MAD)1.8
Skewness18.62276424
Sum22082.87
Variance76.18501347
MonotonicityNot monotonic
2022-10-23T21:12:18.064685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0640
 
0.8%
1.0434
 
0.7%
1.0332
 
0.6%
1.2331
 
0.6%
1.2430
 
0.6%
1.0730
 
0.6%
0.9129
 
0.6%
0.9428
 
0.6%
0.928
 
0.6%
1.127
 
0.5%
Other values (1230)4696
93.8%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.052
< 0.1%
0.061
 
< 0.1%
0.081
 
< 0.1%
0.093
0.1%
0.11
 
< 0.1%
0.133
0.1%
0.143
0.1%
0.153
0.1%
0.164
0.1%
ValueCountFrequency (%)
240.81
< 0.1%
224.651
< 0.1%
221.41
< 0.1%
210.691
< 0.1%
204.921
< 0.1%
177.91
< 0.1%
176.961
< 0.1%
39.952
< 0.1%
33.41
< 0.1%
29.542
< 0.1%

Speed Std
Real number (ℝ≥0)

HIGH CORRELATION

Distinct895
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.606829171
Minimum0.01
Maximum83.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:18.136731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.22
Q10.5
median1.31
Q33.84
95-th percentile7.68
Maximum83.53
Range83.52
Interquartile range (IQR)3.34

Descriptive statistics

Standard deviation3.255171983
Coefficient of variation (CV)1.248709359
Kurtosis139.3239683
Mean2.606829171
Median Absolute Deviation (MAD)1.01
Skewness7.352892921
Sum13047.18
Variance10.59614464
MonotonicityNot monotonic
2022-10-23T21:12:18.203088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4158
 
1.2%
0.4352
 
1.0%
0.4249
 
1.0%
0.4546
 
0.9%
0.4744
 
0.9%
0.3844
 
0.9%
0.3742
 
0.8%
0.4841
 
0.8%
0.5441
 
0.8%
0.3541
 
0.8%
Other values (885)4547
90.8%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.021
 
< 0.1%
0.032
 
< 0.1%
0.042
 
< 0.1%
0.054
 
0.1%
0.065
0.1%
0.075
0.1%
0.0810
0.2%
0.0912
0.2%
0.110
0.2%
ValueCountFrequency (%)
83.531
< 0.1%
70.821
< 0.1%
58.781
< 0.1%
43.411
< 0.1%
29.411
< 0.1%
27.961
< 0.1%
26.321
< 0.1%
24.221
< 0.1%
23.111
< 0.1%
19.691
< 0.1%

Max Acceleration(m/s^2)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct89
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1275704296
Minimum0
Maximum3.55
Zeros80
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:18.271770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.07
Q30.19
95-th percentile0.35
Maximum3.55
Range3.55
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1526249514
Coefficient of variation (CV)1.196397566
Kurtosis87.73006082
Mean0.1275704296
Median Absolute Deviation (MAD)0.06
Skewness5.8891299
Sum638.49
Variance0.02329437578
MonotonicityNot monotonic
2022-10-23T21:12:18.340244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02655
 
13.1%
0.01533
 
10.6%
0.03425
 
8.5%
0.04277
 
5.5%
0.05234
 
4.7%
0.06192
 
3.8%
0.07159
 
3.2%
0.18134
 
2.7%
0.17129
 
2.6%
0.16125
 
2.5%
Other values (79)2142
42.8%
ValueCountFrequency (%)
080
 
1.6%
0.01533
10.6%
0.02655
13.1%
0.03425
8.5%
0.04277
5.5%
0.05234
 
4.7%
0.06192
 
3.8%
0.07159
 
3.2%
0.08117
 
2.3%
0.0978
 
1.6%
ValueCountFrequency (%)
3.551
< 0.1%
2.761
< 0.1%
2.091
< 0.1%
1.931
< 0.1%
1.781
< 0.1%
1.661
< 0.1%
1.641
< 0.1%
1.51
< 0.1%
1.391
< 0.1%
1.381
< 0.1%

95% Acceleration(m/s^2)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09476523477
Minimum0
Maximum1.41
Zeros86
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:18.411861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.06
Q30.15
95-th percentile0.28
Maximum1.41
Range1.41
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.09397332091
Coefficient of variation (CV)0.9916434137
Kurtosis17.8388898
Mean0.09476523477
Median Absolute Deviation (MAD)0.05
Skewness2.296079805
Sum474.3
Variance0.008830985042
MonotonicityNot monotonic
2022-10-23T21:12:18.481572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.02757
15.1%
0.01684
 
13.7%
0.03424
 
8.5%
0.04361
 
7.2%
0.05176
 
3.5%
0.14176
 
3.5%
0.06170
 
3.4%
0.16153
 
3.1%
0.15151
 
3.0%
0.12145
 
2.9%
Other values (39)1808
36.1%
ValueCountFrequency (%)
086
 
1.7%
0.01684
13.7%
0.02757
15.1%
0.03424
8.5%
0.04361
7.2%
0.05176
 
3.5%
0.06170
 
3.4%
0.07130
 
2.6%
0.0895
 
1.9%
0.0984
 
1.7%
ValueCountFrequency (%)
1.411
 
< 0.1%
1.311
 
< 0.1%
1.121
 
< 0.1%
1.051
 
< 0.1%
0.511
 
< 0.1%
0.471
 
< 0.1%
0.461
 
< 0.1%
0.453
0.1%
0.434
0.1%
0.412
< 0.1%

75% Acceleration(m/s^2)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05282517483
Minimum0
Maximum0.45
Zeros325
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:18.551469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.03
Q30.08
95-th percentile0.16
Maximum0.45
Range0.45
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.05453431225
Coefficient of variation (CV)1.0323546
Kurtosis2.552986717
Mean0.05282517483
Median Absolute Deviation (MAD)0.02
Skewness1.525258325
Sum264.39
Variance0.002973991213
MonotonicityNot monotonic
2022-10-23T21:12:18.783605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.011277
25.5%
0.02845
16.9%
0325
 
6.5%
0.08323
 
6.5%
0.03294
 
5.9%
0.04227
 
4.5%
0.09220
 
4.4%
0.1218
 
4.4%
0.06209
 
4.2%
0.12166
 
3.3%
Other values (22)901
18.0%
ValueCountFrequency (%)
0325
 
6.5%
0.011277
25.5%
0.02845
16.9%
0.03294
 
5.9%
0.04227
 
4.5%
0.05120
 
2.4%
0.06209
 
4.2%
0.07165
 
3.3%
0.08323
 
6.5%
0.09220
 
4.4%
ValueCountFrequency (%)
0.451
 
< 0.1%
0.311
 
< 0.1%
0.31
 
< 0.1%
0.283
 
0.1%
0.279
 
0.2%
0.2614
 
0.3%
0.2511
 
0.2%
0.2417
0.3%
0.2319
0.4%
0.2240
0.8%

Mean Acceleration(m/s^2)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03711688312
Minimum0
Maximum0.38
Zeros543
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:18.845861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.06
95-th percentile0.11
Maximum0.38
Range0.38
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.03632634914
Coefficient of variation (CV)0.9787014988
Kurtosis4.462261618
Mean0.03711688312
Median Absolute Deviation (MAD)0.02
Skewness1.552975995
Sum185.77
Variance0.001319603642
MonotonicityNot monotonic
2022-10-23T21:12:18.907015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.011509
30.1%
0.02631
12.6%
0543
 
10.8%
0.06408
 
8.2%
0.05342
 
6.8%
0.07341
 
6.8%
0.03278
 
5.6%
0.04231
 
4.6%
0.08231
 
4.6%
0.09154
 
3.1%
Other values (18)337
 
6.7%
ValueCountFrequency (%)
0543
 
10.8%
0.011509
30.1%
0.02631
12.6%
0.03278
 
5.6%
0.04231
 
4.6%
0.05342
 
6.8%
0.06408
 
8.2%
0.07341
 
6.8%
0.08231
 
4.6%
0.09154
 
3.1%
ValueCountFrequency (%)
0.381
 
< 0.1%
0.331
 
< 0.1%
0.311
 
< 0.1%
0.281
 
< 0.1%
0.241
 
< 0.1%
0.221
 
< 0.1%
0.211
 
< 0.1%
0.22
 
< 0.1%
0.195
0.1%
0.184
0.1%

Acceleration Std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03410589411
Minimum0
Maximum0.75
Zeros584
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:18.972038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.05
95-th percentile0.1
Maximum0.75
Range0.75
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.03706596372
Coefficient of variation (CV)1.086790559
Kurtosis64.72493742
Mean0.03410589411
Median Absolute Deviation (MAD)0.02
Skewness4.792576077
Sum170.7
Variance0.001373885667
MonotonicityNot monotonic
2022-10-23T21:12:19.029717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.011589
31.7%
0584
 
11.7%
0.02527
 
10.5%
0.05439
 
8.8%
0.04434
 
8.7%
0.03314
 
6.3%
0.06290
 
5.8%
0.07238
 
4.8%
0.08167
 
3.3%
0.09125
 
2.5%
Other values (19)298
 
6.0%
ValueCountFrequency (%)
0584
 
11.7%
0.011589
31.7%
0.02527
 
10.5%
0.03314
 
6.3%
0.04434
 
8.7%
0.05439
 
8.8%
0.06290
 
5.8%
0.07238
 
4.8%
0.08167
 
3.3%
0.09125
 
2.5%
ValueCountFrequency (%)
0.751
 
< 0.1%
0.661
 
< 0.1%
0.61
 
< 0.1%
0.491
 
< 0.1%
0.481
 
< 0.1%
0.331
 
< 0.1%
0.321
 
< 0.1%
0.221
 
< 0.1%
0.25
0.1%
0.191
 
< 0.1%

Non 0 Mean Speed(m/s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1248
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.422083916
Minimum0.01
Maximum240.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:19.097127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.58
Q11.1
median2.79
Q35.57
95-th percentile12.624
Maximum240.8
Range240.79
Interquartile range (IQR)4.47

Descriptive statistics

Standard deviation8.732043633
Coefficient of variation (CV)1.974644489
Kurtosis441.6878922
Mean4.422083916
Median Absolute Deviation (MAD)1.81
Skewness18.59820851
Sum22132.53
Variance76.248586
MonotonicityNot monotonic
2022-10-23T21:12:19.171518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0640
 
0.8%
1.0433
 
0.7%
1.0332
 
0.6%
1.2431
 
0.6%
1.2331
 
0.6%
1.0730
 
0.6%
0.9129
 
0.6%
0.9428
 
0.6%
1.127
 
0.5%
0.9727
 
0.5%
Other values (1238)4697
93.8%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.062
< 0.1%
0.071
 
< 0.1%
0.081
 
< 0.1%
0.092
< 0.1%
0.11
 
< 0.1%
0.111
 
< 0.1%
0.133
0.1%
0.143
0.1%
0.153
0.1%
ValueCountFrequency (%)
240.81
< 0.1%
224.651
< 0.1%
221.41
< 0.1%
210.691
< 0.1%
204.921
< 0.1%
177.91
< 0.1%
176.961
< 0.1%
39.952
< 0.1%
34.21
< 0.1%
29.542
< 0.1%

Non 0 Mean Acceleration(m/s^2)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03713486513
Minimum0
Maximum0.38
Zeros546
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:19.236385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.06
95-th percentile0.11
Maximum0.38
Range0.38
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.03634509624
Coefficient of variation (CV)0.978732415
Kurtosis4.448068962
Mean0.03713486513
Median Absolute Deviation (MAD)0.02
Skewness1.550462298
Sum185.86
Variance0.00132096602
MonotonicityNot monotonic
2022-10-23T21:12:19.295440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.011505
30.1%
0.02632
12.6%
0546
 
10.9%
0.06409
 
8.2%
0.05343
 
6.9%
0.07341
 
6.8%
0.03276
 
5.5%
0.08232
 
4.6%
0.04230
 
4.6%
0.09155
 
3.1%
Other values (18)336
 
6.7%
ValueCountFrequency (%)
0546
 
10.9%
0.011505
30.1%
0.02632
12.6%
0.03276
 
5.5%
0.04230
 
4.6%
0.05343
 
6.9%
0.06409
 
8.2%
0.07341
 
6.8%
0.08232
 
4.6%
0.09155
 
3.1%
ValueCountFrequency (%)
0.381
 
< 0.1%
0.331
 
< 0.1%
0.311
 
< 0.1%
0.281
 
< 0.1%
0.241
 
< 0.1%
0.221
 
< 0.1%
0.211
 
< 0.1%
0.22
 
< 0.1%
0.195
0.1%
0.184
0.1%

Total Time(s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2471
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1906.967832
Minimum150
Maximum44135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:19.366972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile208
Q1550
median1150
Q32206
95-th percentile5999
Maximum44135
Range43985
Interquartile range (IQR)1656

Descriptive statistics

Standard deviation2620.808651
Coefficient of variation (CV)1.37433291
Kurtosis37.62805089
Mean1906.967832
Median Absolute Deviation (MAD)711
Skewness4.836316055
Sum9544374
Variance6868637.984
MonotonicityNot monotonic
2022-10-23T21:12:19.433556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20067
 
1.3%
30061
 
1.2%
25056
 
1.1%
15053
 
1.1%
35053
 
1.1%
40045
 
0.9%
45041
 
0.8%
50041
 
0.8%
60031
 
0.6%
65030
 
0.6%
Other values (2461)4527
90.4%
ValueCountFrequency (%)
15053
1.1%
15112
 
0.2%
1528
 
0.2%
1539
 
0.2%
1544
 
0.1%
1555
 
0.1%
1567
 
0.1%
1575
 
0.1%
1582
 
< 0.1%
1591
 
< 0.1%
ValueCountFrequency (%)
441351
< 0.1%
329491
< 0.1%
283821
< 0.1%
274551
< 0.1%
249881
< 0.1%
247921
< 0.1%
240021
< 0.1%
234342
< 0.1%
233881
< 0.1%
228051
< 0.1%

Total Distance(m)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct4790
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11467.7449
Minimum3.74
Maximum4399193.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-10-23T21:12:19.503024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.74
5-th percentile228.972
Q1763.8
median2822.91
Q310137
95-th percentile27953.096
Maximum4399193.91
Range4399190.17
Interquartile range (IQR)9373.2

Descriptive statistics

Standard deviation83245.12971
Coefficient of variation (CV)7.259067102
Kurtosis1720.035948
Mean11467.7449
Median Absolute Deviation (MAD)2382.55
Skewness37.15879244
Sum57396063.21
Variance6929751621
MonotonicityNot monotonic
2022-10-23T21:12:19.570971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1108.783
 
0.1%
745.153
 
0.1%
3028.232
 
< 0.1%
291.412
 
< 0.1%
10057.532
 
< 0.1%
3991.382
 
< 0.1%
3928.942
 
< 0.1%
2844.762
 
< 0.1%
2563.842
 
< 0.1%
3108.792
 
< 0.1%
Other values (4780)4983
99.6%
ValueCountFrequency (%)
3.741
< 0.1%
81
< 0.1%
8.41
< 0.1%
17.891
< 0.1%
24.621
< 0.1%
25.431
< 0.1%
30.261
< 0.1%
32.851
< 0.1%
38.61
< 0.1%
40.661
< 0.1%
ValueCountFrequency (%)
4399193.911
< 0.1%
2341516.711
< 0.1%
1615898.861
< 0.1%
1439325.731
< 0.1%
1110808.391
< 0.1%
950665.31
< 0.1%
732962.091
< 0.1%
379360.191
< 0.1%
328197.851
< 0.1%
324719.291
< 0.1%

Interactions

2022-10-23T21:12:16.363755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.338081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.284465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.126465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.040099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.873760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.775008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.705824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.630602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.562248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.440057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.336114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.212874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.518978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.422889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.496654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.343721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.185974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.099708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.931396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.838532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.777504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.689990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.625168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.505394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.399216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.286971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.582130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.479966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.555983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.401693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.317033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.157428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.987690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.901989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.840141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.748617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.686632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.565886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.461026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.362855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.642381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.536938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.615145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.461812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.374546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.216971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.043269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.971850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.901063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.805485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.747515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.626264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.522260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.430025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.704875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.593922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.673663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.520899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.432365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.274527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.099375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.035150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.962667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.862996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.808849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.687427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.589685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.499177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.767191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.650679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.730897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.579761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.487488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.330084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.152830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.111584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.022504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.918175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.867634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.745109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.649956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.563087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.825128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.712452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.794387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.642320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.550911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.392787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.214322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.182830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.088044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.980805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.933618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.810340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.721601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.655316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.887691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.774472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.858787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.705098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.613188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.455419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.275834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.248965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.153273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.043242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.999455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.875297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.791547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.895144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.949149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.831177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.917457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.762821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.672602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.512884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.335001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.309949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.214121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.100043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.060228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.935917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.852307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.986631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.006475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.893231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:04.982014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.826401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.741564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.575804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.396597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.377639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.285424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.162706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.126242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.002784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.915380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.110376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.069458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.955890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.045239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.889489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.805014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.638786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.542918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.449850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.365247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.224431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.192003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.070557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.978399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.189574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.132779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:17.014923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.104477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.947478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.862428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.696388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.599746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.516463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.426065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.387140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.253583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.134879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.035888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.309798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.189501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:17.077530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.169301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.013273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.926193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.760048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.663162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.586738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.499890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.449810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.320228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.206625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.099298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.387578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.251868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:17.132239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:05.226162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.069386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:06.982664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:07.816477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:08.719242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:09.645727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:10.565462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:11.505206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:12.379562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:13.270974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:14.154913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:15.453587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-23T21:12:16.307445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-23T21:12:19.634702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-23T21:12:19.746847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-23T21:12:19.856291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-23T21:12:19.968477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-23T21:12:17.231640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-23T21:12:17.382059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Transportation ModeMax Speed(m/s)95% Speed(m/s)75% Speed(m/s)Mean Speed(m/s)Speed StdMax Acceleration(m/s^2)95% Acceleration(m/s^2)75% Acceleration(m/s^2)Mean Acceleration(m/s^2)Acceleration StdNon 0 Mean Speed(m/s)Non 0 Mean Acceleration(m/s^2)Total Time(s)Total Distance(m)
0bus12.1810.465.873.413.830.190.110.040.030.053.410.0338116465.72
1bus15.9715.8415.5111.805.180.190.140.070.040.0511.800.049399533.53
2walk1.831.721.511.340.290.010.010.010.000.001.340.00601791.24
3bus12.7811.979.676.783.560.160.160.080.060.056.780.068665771.59
4walk1.501.501.451.340.140.010.010.000.000.001.340.00588783.50
5walk2.682.611.571.420.760.030.030.020.010.011.420.016601045.96
6bus8.228.096.444.692.520.080.080.060.040.034.690.0410595295.50
7walk3.662.111.651.540.580.060.050.010.010.021.540.0110801668.44
8bus20.7116.996.746.006.340.260.230.160.120.086.000.129833817.19
9walk1.381.371.341.110.300.010.010.010.010.011.110.01305329.35

Last rows

Transportation ModeMax Speed(m/s)95% Speed(m/s)75% Speed(m/s)Mean Speed(m/s)Speed StdMax Acceleration(m/s^2)95% Acceleration(m/s^2)75% Acceleration(m/s^2)Mean Acceleration(m/s^2)Acceleration StdNon 0 Mean Speed(m/s)Non 0 Mean Acceleration(m/s^2)Total Time(s)Total Distance(m)
4995walk1.141.141.131.020.120.010.010.000.000.001.020.00306312.15
4996bus5.905.844.442.901.920.110.090.050.040.032.900.047652218.28
4997walk2.931.531.351.200.390.060.010.010.010.011.200.0151533643.89
4998taxi18.7318.1012.989.295.110.140.130.100.060.049.290.06135012539.55
4999walk2.011.981.851.450.540.020.020.010.010.001.450.01309687.59
5000walk1.171.161.140.910.370.010.010.000.000.000.910.00317276.02
5001bus8.627.304.233.632.150.080.080.050.040.023.630.045822030.97
5002walk1.411.341.120.980.260.020.020.010.010.010.980.01301296.35
5003bike8.328.233.722.882.450.140.090.040.030.042.880.0310012886.13
5004walk1.431.261.020.750.340.020.020.010.010.010.750.01639487.62

Duplicate rows

Most frequently occurring

Transportation ModeMax Speed(m/s)95% Speed(m/s)75% Speed(m/s)Mean Speed(m/s)Speed StdMax Acceleration(m/s^2)95% Acceleration(m/s^2)75% Acceleration(m/s^2)Mean Acceleration(m/s^2)Acceleration StdNon 0 Mean Speed(m/s)Non 0 Mean Acceleration(m/s^2)Total Time(s)Total Distance(m)# duplicates
0bike2.401.331.060.800.430.040.020.010.010.010.800.0140452844.762
1bike2.462.281.601.040.800.040.040.030.020.011.040.02550570.852
2bike2.692.481.861.440.680.020.020.020.010.001.440.01350504.132
3bike3.012.952.501.921.000.040.040.020.020.011.920.021025809.772
4bike3.113.052.962.440.830.050.040.010.010.022.440.015081111.632
5bike3.263.001.981.351.220.060.060.040.030.021.350.03368296.062
6bike3.263.042.412.130.740.050.040.020.020.022.130.02351745.152
7bike3.473.042.001.281.050.060.060.020.020.021.280.02598662.192
8bike3.533.443.122.510.790.060.040.020.020.012.510.0238145676.692
9bike3.623.573.402.850.810.040.040.040.020.022.850.02300855.772